Machine Learning techniques for synthetic data generation in Energy and Financial Markets

The availability of sufficiently large, reliable, and high-quality datasets represents a fundamental prerequisite for quantitative analysis and data-driven decision-making in economics and finance. In practice, however, financial data are often limited, noisy, or subject to restricted access, creating significant empirical constraints for both researchers and practitioners. Recent advances in Generative Machine Learning (GenML) provide promising tools to overcome these limitations by enabling the generation of synthetic data capable of preserving the main statistical features of original data. Despite the rapid diffusion of these techniques, most existing studies focus on replicating stylized facts of financial time series or producing forward-looking simulations, while less attention has been devoted to a systematic assessment of the generative fidelity and generalization capacity of alternative models across different distributional environments. Motivated by this gap, this study provides a comparative evaluation of several Deep Generative Machine Learning (Deep-GenML) families by assessing their ability to reproduce both theoretical statistical distributions and empirical financial and commodity market data. The analysis spans multiple Deep-GenML architectures, distributional settings and market regimes, while also examining model performance under alternative training configurations that reflect varying degrees of data availability. The empirical evidence indicates that deep generative models are capable of accurately reproducing complex distributional features—including heavy tails, asymmetry, and multimodality—across a wide range of scenarios. Overall, the results highlight the potential of deep generative approaches as flexible tools for synthetic data generation and distributional modeling in financial and energy market applications.